code: def mass_distance_profile_parallel(
      indices, tsA=None, tsB=None, m=None
  ):
      """
      Computes distance profiles for the given indices either via self join or similarity search.
  
      Parameters
      ----------
      indices: Array of indices to compute distance profile for.
      tsA: Time series containing the query for which to calculate the distance profile.
      tsB: Time series to compare the query against. Note that, for the time being, only tsB = tsA is allowed
      m: Length of query.
      """
      distance_profiles = []
  
      for index in indices:
          distance_profiles.append(
              massDistanceProfile(tsA, index, m, tsB=tsB)
          )
  
      return distance_profiles
  
file_path: matrixprofile-ts/matrixprofile/distanceProfile.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from .utils import *
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: mass_distance_profile_parallel(indices, tsA, tsB, m) and returns: distance_profiles